Synthetic Data
Test on data that behaves like production, without touching one real record.
High-quality synthetic datasets replicate the statistical properties of real customer data, bank statements, identity documents, fraud, loans, so teams validate meaningfully while privacy stays intact.
The data library
200+ synthetic and public datasets, ready to load into any workspace.
Bank statements
Transaction histories across retail and SME profiles.
Identity documents
Document sets for KYC, onboarding and verification flows.
Fraudulent transactions
Labelled fraud patterns for model training and bake-offs.
Loan applications
Application and decisioning data for credit-risk testing.
Payments & cards
Card, merchant and payment-flow data for payment tech.
On-chain & digital assets
Synthetic on-chain activity for crypto and AML testing.
The principle
Useful synthetic data isn’t clean. It’s deliberately messy.
A vendor demo runs on the happy path. Real production doesn’t. The point of synthetic data isn’t a tidy spreadsheet, it’s representative data with the missing records, fragmented histories and multi-provider gaps your teams actually face, so you find out how a tool behaves against reality before you commit.
Edge cases on purpose. Missing fields, incomplete records, anomalies and conflicting sources, the conditions that break naive tools.
Real coverage. Built around genuine personas, scenarios and geographies, so “representative” actually means something.
Straight into beta. Realistic personas remove the single biggest blocker to getting a working build in front of pilot users.
synthetic_dataset · caregiver_v3
VALIDATING
patient_0142
patient_0145
patient_0148
patient_0151
patient_0154
patient_0157
patient_0160
Populated
Intentional gap
Anomaly
What our customers say
Teams move faster when the data isn’t the blocker.
“Our collaboration with NayaOne has dramatically streamlined how we vet fintech vendors, positioning us well ahead in the digital transformation and AI race.”
Head of Innovation
Top-10 global bank
“Test data is a challenge all the time, and we’ve got to get systems in sync. This removes that friction at the exact moment we need to move.”
CIO, Group Insurance
North American insurer
“NayaOne shows what’s possible when ambition meets execution, turning bold ideas into measurable industry impact.”
Innovation Director
European financial group
In practice
A caregiver-support AI, validated on data that was deliberately messy.
A North American insurer building a new caregiver proposition needed to test an AI assistant against the reality its users face, not a clean demo set. NayaOne generated a synthetic healthcare dataset spanning 25 caregiver personas across multiple health systems and states.
It deliberately included missing records, fragmented histories and multi-provider gaps, so the team could prove the AI held up against messy real-world conditions, then move straight into beta with realistic personas. No real patient record was ever exposed.
1 week
to generate the synthetic dataset
3 days
to a ready environment with the tech stack
25
caregiver personas across real scenarios
0
real patient records exposed
Common questions
Q.
Is synthetic data actually representative?
It replicates the statistical properties and edge cases of real datasets, distributions, correlations, anomalies, so models and integrations behave as they would in production, without the exposure.
Q.
Can we bring our own data instead?
Yes, under your controls, inside the air-gap. Most teams start with synthetic to move fast, then introduce their own data once the approach is proven.
Q.
Does this satisfy our privacy and compliance teams?
Synthetic data carries no real customer information, so validation happens without privacy risk. It’s why regulators, including the FCA Digital Sandbox, rely on the approach.